Results 1 to 10 of about 183,384 (114)

An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction [PDF]

Bioengineering, 2023
(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain ...
Zishan Wang, Ruqiang Huang, Ye Yan, Zhiguo Luo, Shaokai Zhao, Bei Wang, Jing Jin, Liang Xie, Erwei Yin   +8 more
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Multimodal Brain Growth Patterns: Insights from Canonical Correlation Analysis and Deep Canonical Correlation Analysis with Auto-Encoder [PDF]

Information
Today’s advancements in neuroimaging have been pivotal in enhancing our understanding of brain development and function using various MRI techniques.
Ram Sapkota, Bishal Thapaliya, Bhaskar Ray, Pranav Suresh, Jingyu Liu   +4 more
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Canonical correlation analysis for multi-omics: Application to cross-cohort analysis. [PDF]

PLoS Genetics, 2023
Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest.
Min-Zhi Jiang, François Aguet, Kristin Ardlie, Jiawen Chen, Elaine Cornell, Dan Cruz, Peter Durda, Stacey B Gabriel, Robert E Gerszten, Xiuqing Guo, Craig W Johnson, Silva Kasela, Leslie A Lange, Tuuli Lappalainen, Yongmei Liu, Alex P Reiner, Josh Smith, Tamar Sofer, Kent D Taylor, Russell P Tracy, David J VanDenBerg, James G Wilson, Stephen S Rich, Jerome I Rotter, Michael I Love, Laura M Raffield, Yun Li, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Analysis Working Group   +27 more
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Permutation inference for canonical correlation analysis

NeuroImage, 2020
Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements.
Anderson M. Winkler, Olivier Renaud, Stephen M. Smith, Thomas E. Nichols   +3 more
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Structure-adaptive canonical correlation analysis for microbiome multi-omics data [PDF]

Frontiers in Genetics
Sparse canonical correlation analysis (sCCA) has been a useful approach for integrating different high-dimensional datasets by finding a subset of correlated features that explain the most correlation in the data.
Linsui Deng, Yanlin Tang, Xianyang Zhang, Jun Chen   +3 more
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Chunk Incremental Canonical Correlation Analysis [PDF]

Jisuanji kexue yu tansuo, 2022
For the large-scale dynamic data stream, incremental learning is an effective and efficient technique and is widely used in machine learning. Incremental dimensionality reduction algorithms have been proposed by many scholars.
PAN Yu, CHEN Xiaohong, LI Shunming, LI Jiyong
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Canonical Concordance Correlation Analysis

Mathematics, 2022
A multivariate technique named Canonical Concordance Correlation Analysis (CCCA) is introduced. In contrast to the classical Canonical Correlation Analysis (CCA) which is based on maximization of the Pearson’s correlation coefficient between the linear ...
Stan Lipovetsky
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Supervised Canonical Correlation Analysis Based on Deep Learning [PDF]

Jisuanji gongcheng, 2022
Canonical Correlation Analysis (CCA) is a multivariate statistical method, which uses the correlation between comprehensive variable pairs to reflect the overall correlation between two groups of indicators.The traditional CCA method can not effectively ...
ZHANG Heng, CHEN Xiaohong, LAN Yuxiang, LI Shunming
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Incremental Canonical Correlation Analysis

Applied Sciences, 2020
Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations.
Hongmin Zhao, Dongting Sun, Zhigang Luo
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Canonical Correlation Analysis to Biomass CHONS Prediction

Chemical Engineering Transactions, 2023
Fermentation biomasses can be defined as a complex mixture of different natural components and microbes, having biodegradable and organic waste as the primary source. Its correct characterization is crucial to have proper processing in fermentative units.
Federico Moretta, Vincenzo Del Duca, Giulia Bozzano, Antonio Coppola, Fabrizio Scala   +4 more
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